CN115701695A

CN115701695A - Method and device for determining channel statistical covariance

Info

Publication number: CN115701695A
Application number: CN202110879764.2A
Authority: CN
Inventors: 孟鑫; 严冠文; 蓝瑞宁; 秦晨翔; 杨烨
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2021-08-02
Filing date: 2021-08-02
Publication date: 2023-02-10
Also published as: WO2023011075A1

Abstract

The application relates to the technical field of communication, and provides a method and a device for determining channel statistical covariance, which are used for accurately determining the statistical covariance of a channel. And the network equipment carries out channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix. Then, transforming the uplink channel estimation matrix based on the first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is associated with an uplink channel. Determining first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: and performing statistical averaging on the energy corresponding to part or all of the elements in the first channel estimation matrix respectively to obtain the energy. Next, determining a first power spectrum based on the first statistical average energy, wherein a mapping relation exists between the first statistical average energy and the first power spectrum; determining a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transform matrix is associated with a downlink channel.

Description

Method and device for determining channel statistical covariance

Technical Field

The embodiment of the application relates to the field of communication and the like, in particular to a method and a device for determining channel statistical covariance.

Background

The multi-antenna system configures a plurality of transceiving antennas on a device (e.g., a network device), and exploits and utilizes space dimension resources to increase system capacity. A key factor for increasing the downlink capacity of the multi-antenna system is to obtain more accurate Channel State Information (CSI) at the transmitting end.

In order to more accurately obtain (obtain may also be understood as estimate) the channel state information CSI, the obtaining may be performed using statistical information of the channel, in particular using statistical covariance information of the channel.

Based on this, how to determine the statistical covariance of the channel is a technical problem to be solved.

Disclosure of Invention

The embodiment of the application provides a method and a device for determining channel statistical covariance, which are used for accurately determining the statistical covariance of a channel.

In a first aspect, a method for determining channel statistical covariance is provided, where an execution subject of the method may be a network device, or a component, such as a chip, a processor, and the like, applied in the network device. The following description will be made taking an example in which the execution subject is a network device. And the network equipment carries out channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix. Then, the network equipment transforms the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to an uplink channel. Then, the network equipment determines a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: and performing statistical averaging on the energy corresponding to part or all of the elements in the first channel estimation matrix respectively to obtain the energy. Next, the network device determines a first power spectrum based on the first statistical average energy, wherein a mapping relationship exists between the first statistical average energy and the first power spectrum. Then, the network equipment determines a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to a downlink channel.

In a first aspect, an uplink channel estimation matrix is transformed, and the average energy of the transformed matrix is counted, followed by determining a power spectrum based on the average energy. And then, based on the determined power spectrum, obtaining a statistical covariance matrix of the downlink channel. The method is simple and can accurately determine the statistical covariance of the channel.

In a possible implementation, the network device may further transmit data and/or reference signals based on the statistical covariance matrix of the downlink channel.

In one possible implementation, each element in the first power spectrum is a non-negative real number value. Therefore, the semi-positive determination of the determined statistical covariance matrix of the downlink channel can be ensured, and the accuracy of the determined statistical covariance matrix of the downlink channel can be improved.

In one possible implementation, the first transformation matrix is any one of: the system comprises a first Discrete Cosine Transform (DCT) matrix, a first Hadamard transform (Hadamard transform) matrix, a first Discrete Fourier Transform (DFT) matrix and a first oversampling DFT matrix. The second transformation matrix is any one of: a second Discrete Cosine Transform (DCT) matrix, a second Hadamard transform matrix, a second Discrete Fourier Transform (DFT) matrix, and a second oversampled DFT matrix.

The first transform matrix and the second transform matrix may be of the same type, e.g. both Discrete Cosine Transform (DCT) matrices, or both Hadamard transform matrices, and the content of the first transform matrix and the second transform matrix may be the same or different. Alternatively, the first transform matrix and the second transform matrix may be of different types, for example the first transform matrix is a discrete cosine transform, DCT, matrix and the second transform matrix is a hadamard transform matrix.

In one possible implementation, the first transformation matrix is obtained based on at least one of: a first spatial domain transform matrix, a first frequency domain transform matrix, a first time domain transform matrix. The second transformation matrix is obtained based on at least one of: a second spatial domain transform matrix, a second frequency domain transform matrix, and a second time domain transform matrix.

The matrix types used to obtain the first transformation matrix and the second transformation matrix are the same, e.g., both are spatial domain transformation matrices, or both are time domain transformation matrices. The matrix content used to obtain the first transformation matrix and the second transformation matrix may be the same or different. Alternatively, the matrix types used to obtain the first transformation matrix and the second transformation matrix are different, e.g., the first transformation matrix is obtained based on a spatial domain transformation matrix and the second transformation matrix is obtained based on a temporal domain transformation matrix.

The determining method can be suitable for the scene of one or more statistical covariance in the space domain, the frequency domain and the time domain, and is easy to popularize.

In one possible implementation, the mapping relationship between the first statistical average energy and the first power spectrum satisfies the following equation: t ω = Φ, where ω is the first power spectrum, Φ is the first statistical average energy, T is a mapping matrix, and T is associated with the first transformation matrix.

In a second aspect, a method for determining channel statistical covariance is provided, where an execution subject of the method may be a terminal device, and may also be a component, such as a chip, a processor, and the like, applied in the terminal device. The following description will be made taking as an example that the execution subject is a terminal device. And the terminal equipment carries out channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix. Then, the terminal equipment transforms the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to a downlink channel. Then, the terminal equipment determines second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: and performing statistical averaging on the energy corresponding to part or all of the elements in the second channel estimation matrix respectively to obtain the energy. Next, the terminal device determines a second power spectrum based on the second statistical average energy, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum. Then, the terminal equipment determines a statistical covariance matrix of an uplink channel based on the second power spectrum and the fourth transformation matrix; the fourth transformation matrix is a matrix related to an uplink channel.

In a possible implementation, the terminal device may further send data and/or reference signals based on the statistical covariance matrix of the uplink channel.

In one possible implementation, each element in the second power spectrum is a non-negative real value.

In one possible implementation, the third transformation matrix is any one of: a third Discrete Cosine Transform (DCT) matrix, a third Hadamard transform (Hadamard transform) matrix, a third Discrete Fourier Transform (DFT) matrix and a third oversampled DFT matrix. The fourth transformation matrix is any one of: a fourth Discrete Cosine Transform (DCT) matrix, a fourth Hadamard transform (Hadamard transform) matrix, a fourth Discrete Fourier Transform (DFT) matrix and a fourth oversampled Discrete Fourier Transform (DFT) matrix.

The third transform matrix and the fourth transform matrix may be of the same type, for example both DCT matrices or both hadamard transform matrices, and may or may not have the same content. Alternatively, the type of the third transform matrix and the fourth transform matrix may be different, for example, the third transform matrix is a Discrete Cosine Transform (DCT) matrix and the fourth transform matrix is a Hadamard transform matrix.

In one possible implementation, the third transformation matrix is obtained based on at least one of: a third spatial domain transform matrix, a third frequency domain transform matrix, and a third time domain transform matrix. The fourth transformation matrix is obtained based on at least one of: a fourth spatial domain transformation matrix, a fourth frequency domain transformation matrix, and a fourth time domain transformation matrix.

The matrix types used to obtain the third transformation matrix and the fourth transformation matrix are the same, e.g., both are spatial domain transformation matrices, or both are time domain transformation matrices. The matrix content used to obtain the third transformation matrix and the fourth transformation matrix may be the same or different. Alternatively, the matrix types used to obtain the third transformation matrix and the fourth transformation matrix are different, e.g., the third transformation matrix is obtained based on a spatial domain transformation matrix and the fourth transformation matrix is obtained based on a temporal domain transformation matrix.

In one possible implementation, the mapping relationship between the second statistical mean energy and the second power spectrum satisfies the following formula: t ω = Φ, where ω is the second power spectrum, Φ is the second statistical mean energy, T is a mapping matrix, and T is associated with the third transformation matrix.

A third aspect provides a communication device having functionality for implementing any one of the above-described first aspect and possible implementations of the first aspect, or for implementing any one of the above-described second aspect and possible implementations of the second aspect. These functions may be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more functional modules corresponding to the above functions.

For example, when the apparatus has the function of implementing the first aspect and any possible implementation of the first aspect, the apparatus includes:

the interface module is used for receiving uplink reference signals;

the processing module is used for carrying out channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; transforming the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to an uplink channel; determining a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: performing statistical averaging on energy corresponding to part or all elements in the first channel estimation matrix respectively to obtain the energy; determining a first power spectrum based on the first statistical average energy, wherein a mapping relation exists between the first statistical average energy and the first power spectrum; determining a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to a downlink channel.

In an example, the interface module is further configured to send data and/or reference signals based on the statistical covariance matrix of the downlink channel.

Illustratively, when the apparatus has the function of implementing any one of the second aspect and the second possible implementation, the apparatus includes:

the interface module is used for receiving a downlink reference signal;

the processing module is used for carrying out channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; transforming the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to a downlink channel; determining a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: performing statistical averaging on energies corresponding to part or all elements in the second channel estimation matrix to obtain the energy; determining a second power spectrum based on the second statistical average energy, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; determining a statistical covariance matrix of an uplink channel based on the second power spectrum and a fourth transformation matrix; the fourth transformation matrix is a matrix related to an uplink channel.

In an example, the interface module is further configured to send data and/or reference signals based on the statistical covariance matrix of the uplink channel.

In a fourth aspect, a communications apparatus is provided that includes a processor, and optionally, a memory; the processor and the memory are coupled; the memory for storing computer programs or instructions; the processor is configured to execute part or all of the computer program or instructions in the memory, and when the part or all of the computer program or instructions are executed, the processor is configured to implement the function of the terminal device in the method according to any one of the above-mentioned first aspect and first possible implementation, or implement the function of the first network element in any one of the above-mentioned second aspect and second possible implementation.

In a possible implementation, the apparatus may further include a transceiver configured to transmit a signal processed by the processor or receive a signal input to the processor. The transceiver may perform the sending action or the receiving action performed by the terminal device in any possible implementation of the first aspect and the first aspect; or, perform the sending action or the receiving action performed by the first network element in the second aspect and any possible implementation of the second aspect.

In a fifth aspect, the present application provides a chip system, which includes one or more processors (also referred to as processing circuits) electrically coupled to a memory (also referred to as a storage medium); the memory may or may not be located in the system-on-chip; the memory for storing computer programs or instructions; the processor is configured to execute part or all of the computer program or instructions in the memory, and when the part or all of the computer program or instructions is executed, the processor is configured to implement the function of the terminal device in the method according to any one of the foregoing first aspect and the foregoing first possible implementation, or implement the function of the first network element in any one of the foregoing second aspect and the foregoing second possible implementation.

In a possible implementation, the chip system may further include an input/output interface (also referred to as a communication interface) for outputting a signal processed by the processor or receiving a signal input to the processor. The input/output interface may perform a sending action or a receiving action performed by the terminal device in any possible implementation of the first aspect and the first aspect; or, perform the sending action or the receiving action performed by the first network element in the second aspect and any possible implementation of the second aspect. Specifically, the output interface performs the sending action, and the input interface performs the receiving action.

In one possible implementation, the system-on-chip may be formed by a chip, or may include a chip and other discrete devices.

A sixth aspect provides a computer readable storage medium for storing a computer program comprising instructions for implementing the functions of the first aspect and any possible implementation of the first aspect, or for implementing the functions of the second aspect and any possible implementation of the second aspect.

Alternatively, a computer-readable storage medium is used for storing a computer program, and when the computer program is executed by a computer, the computer may be caused to execute the method performed by the terminal device in the method according to any one of the above-mentioned first aspect and the first possible implementation, or execute the method performed by the first network element in any one of the above-mentioned second aspect and the second possible implementation.

In a seventh aspect, a computer program product is provided, the computer program product comprising: computer program code for causing a computer to perform the method performed by the terminal device in any of the above described first aspect and possible implementations of the first aspect, or the method performed by the first network element in any of the above described second aspect and possible implementations of the second aspect, when said computer program code is run on a computer.

In an eighth aspect, a communication system is provided, where the communication system includes a terminal device in a method for performing any one of the above first aspect and possible implementations of the first aspect, and a first network element in a method for performing any one of the above second aspect and possible implementations of the second aspect.

For technical effects of the third to eighth aspects, reference may be made to the descriptions of the first to second aspects, and repeated descriptions are omitted.

Drawings

Fig. 1 is a diagram of a communication system architecture provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of communication based on statistical covariance of a downlink channel according to an embodiment of the present application;

fig. 3 is a schematic diagram of a process for determining a statistical covariance of a downlink channel according to an embodiment of the present application;

fig. 4 is a schematic diagram of another process for determining the statistical covariance of the downlink channel according to an embodiment of the present application;

fig. 5 is a schematic flowchart of communication based on statistical covariance of an uplink channel according to an embodiment of the present application;

fig. 6 is a schematic diagram of a process of determining statistical covariance of an uplink channel according to an embodiment of the present application;

fig. 7 is a diagram illustrating a structure of a communication device according to an embodiment of the present application;

fig. 8 is a block diagram of another communication apparatus provided in the embodiment of the present application.

Detailed Description

In order to facilitate understanding of the technical solutions of the embodiments of the present application, a system architecture of the method provided by the embodiments of the present application will be briefly described below. It can be understood that the system architecture described in the embodiment of the present application is for more clearly explaining the technical solutions in the embodiment of the present application, and does not constitute a limitation on the technical solutions provided in the embodiment of the present application.

The technical scheme of the embodiment of the application can be applied to various communication systems, for example: satellite communication system, conventional mobile communication system. Wherein the satellite communication system can be integrated with a conventional mobile communication system (i.e., a terrestrial communication system). Communication systems are for example: wireless Local Area Network (WLAN) communication system, wireless fidelity (WiFi) system, long Term Evolution (LTE) system, LTE Frequency Division Duplex (FDD) system, LTE Time Division Duplex (TDD) system, fifth generation (5 g) system, new Radio (NR), sixth generation (6 g) system, and other future communication systems, etc., and also supports a communication system in which multiple wireless technologies are integrated, for example, a system in which non-ground-based network (non-terrestrial network, NTN) integrated ground mobile communication network such as unmanned aerial vehicle, satellite communication system, and High Altitude Platform (HAPS) communication is also applicable.

Fig. 1 is a schematic architecture diagram of a communication system 1000 to which an embodiment of the present application is applied. As shown in fig. 1, the communication system includes a radio access network 100 and a core network 200, and optionally, the communication system 1000 may further include an internet 300.

The radio access network 100 may include at least one radio access network device (e.g., 110a and 110b in fig. 1) and may further include at least one terminal (e.g., 120a-120j in fig. 1). The terminal is connected with the wireless access network equipment in a wireless mode, and the wireless access network equipment is connected with the core network in a wireless or wired mode. The core network device and the radio access network device may be separate physical devices, or the function of the core network device and the logical function of the radio access network device may be integrated on the same physical device, or a physical device may be integrated with a part of the function of the core network device and a part of the function of the radio access network device. The terminals and the radio access network devices can be connected with each other in a wired or wireless mode. Fig. 1 is a schematic diagram, and other network devices, such as a wireless relay device and a wireless backhaul device, may also be included in the communication system, which are not shown in fig. 1.

The radio access network device may be a base station (base station), an evolved NodeB (eNodeB), a Transmission Reception Point (TRP), a next generation base station (next generation NodeB, gNB) in a fifth generation (5th generation, 5g) mobile communication system, a next generation base station in a sixth generation (6th generation, 6g) mobile communication system, a base station in a future mobile communication system, an access node in a WiFi system, or the like; the present invention may also be a module or a unit that performs part of the functions of the base station, for example, a Centralized Unit (CU) or a Distributed Unit (DU). The radio access network device may be a macro base station (e.g., 110a in fig. 1), a micro base station or an indoor station (e.g., 110b in fig. 1), a relay node or a donor node, and the like. It is understood that all or part of the functions of the radio access network device in the present application may also be implemented by software functions running on hardware, or by virtualized functions instantiated on a platform (e.g., a cloud platform). The embodiments of the present application do not limit the specific technologies and the specific device forms adopted by the radio access network device. For convenience of description, the following description will be made with a base station as an example of the radio access network device.

A terminal may also be referred to as a terminal equipment, user Equipment (UE), a mobile station, a mobile terminal, etc. The terminal can be widely applied to various scenes, for example, device-to-device (D2D), vehicle-to-electrical (V2X) communication, machine-type communication (MTC), internet of things (IOT), virtual reality, augmented reality, industrial control, automatic driving, telemedicine, smart grid, smart furniture, smart office, smart wearing, smart transportation, smart city, and the like. The terminal can be cell-phone, panel computer, take the computer of wireless transceiving function, wearable equipment, vehicle, unmanned aerial vehicle, helicopter, aircraft, steamer, robot, arm, intelligent house equipment etc.. The embodiment of the present application does not limit the specific technology and the specific device form adopted by the terminal.

The base stations and terminals may be fixed or mobile. The base station and the terminal can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on the water surface; it may also be deployed on airborne airplanes, balloons and satellite vehicles. The embodiment of the application does not limit the application scenarios of the base station and the terminal.

The roles of base station and terminal may be relative, e.g., helicopter or drone 120i in fig. 1 may be configured to move the base station, for those terminals 120j that access radio access network 100 through 120i, terminal 120i is the base station; however, for the

base station

110a, 120i is a terminal, i.e. the

base station

110a and 120i communicate with each other via a radio interface protocol. Of course, 110a and 120i may communicate with each other through an interface protocol between the base station and the base station, and in this case, 120i is also the base station with respect to 110 a. Therefore, the base station and the terminal may be collectively referred to as a communication apparatus, 110a and 110b in fig. 1 may be referred to as a communication apparatus having a base station function, and 120a to 120j in fig. 1 may be referred to as a communication apparatus having a terminal function.

The base station and the terminal, the base station and the base station, and the terminal can communicate through the authorized spectrum, the unlicensed spectrum, or both the authorized spectrum and the unlicensed spectrum; communication may be performed in a frequency spectrum of 6 gigahertz (GHz) or less, in a frequency spectrum of 6GHz or more, or in a frequency spectrum of 6GHz or less and in a frequency spectrum of 6GHz or more. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

In the embodiments of the present application, the functions of the base station may also be performed by a module (e.g., a chip) in the base station, or may also be performed by a control subsystem including the functions of the base station. The control subsystem including the base station function may be a control center in an application scenario of the terminal, such as a smart grid, industrial control, intelligent transportation, and smart city. The functions of the terminal may be performed by a module (e.g., a chip or a modem) in the terminal, or by a device including the functions of the terminal.

In the application, a base station sends a downlink signal or downlink information to a terminal, and the downlink information is carried on a downlink channel; the terminal sends uplink signals or uplink information to the base station, and the uplink information is carried on an uplink channel.

For understanding the embodiment of the present application, an application scenario of the present application is introduced next, and a network architecture and a service scenario described in the embodiment of the present application are for more clearly explaining the technical solution of the embodiment of the present application, and do not form a limitation on the technical solution provided in the embodiment of the present application.

In general, a multi-antenna system configures multiple transmit/receive antennas at a network device, so as to exploit and utilize space dimension resources to increase system capacity. A key factor for improving the downlink capacity of the multi-antenna system is to obtain more accurate downlink Channel State Information (CSI) at the network device side.

In a Time Division Duplex (TDD) system, after a channel is calibrated, because there is reciprocity between an uplink channel and a downlink channel, a downlink Channel State Information (CSI) may be estimated through an uplink Sounding Reference Signal (SRS) sent by a terminal device. If the channel of the time division duplex TDD system is not calibrated or the calibration error is large, the uplink and downlink equivalent baseband channel between the network equipment and the terminal equipment has no reciprocity, and the downlink channel state information CSI needs to be fed back to the network equipment by the terminal equipment.

A Frequency Division Duplex (FDD) system has no channel reciprocity due to the difference between uplink and downlink frequency points (e.g., uplink 2.1G and downlink 3.5G), and the downlink channel state information CSI can only be fed back to the network device through the terminal device.

In the downlink channel state information CSI feedback process, in an example, the network device sends a downlink channel state information reference signal (CSI-RS). The terminal equipment estimates a downlink channel based on the received downlink CSI-RS, selects a codebook index which is most matched with the downlink channel from a predefined codebook set, and then feeds back the selected codebook index to the network equipment through an uplink channel. Limited by uplink feedback overhead, the terminal device quantizes the real channel using the finite-state codebook, and an unavoidable quantization error exists between the codebook and the real channel, which may limit the accuracy of the network device to obtain (obtain may also be referred to as estimate) the CSI.

In order to more accurately acquire the downlink channel state information CSI, the acquisition may be performed by using statistical information of the downlink channel, and particularly, statistical covariance information of the downlink channel.

As shown in fig. 2, a flow chart of communication based on the statistical covariance of the downlink channel is introduced.

Some or all of the antenna(s) in the terminal device transmit uplink reference signals (e.g., SRS) to the network device. The network equipment carries out channel estimation based on the received uplink reference signal, and estimates an uplink channel estimation matrix of a channel between each transmitting antenna of the terminal equipment and the network equipment. The uplink channel estimation matrix may be a matrix or a vector (vector is a one-dimensional matrix).

Then, the network device determines a statistical covariance matrix of the downlink channel based on the uplink channel estimation matrix.

The statistical covariance matrix of the downlink channel can be used for downlink pilot weighting, and then the downlink reference signal is sent. In addition, the statistical covariance matrix of the downlink channel can be used for precoding of a single user/multiple users, and then downlink data is sent.

Next, in order to facilitate understanding of the embodiments of the present application, some terms of the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.

1) Power spectrum, representing the physical characteristics of the channel. The first power spectrum hereinafter may be one or more combinations of an angle power spectrum, a time delay power spectrum, a doppler power spectrum. The second power spectrum hereinafter may be one or more combinations of an angle power spectrum, a time delay power spectrum, a doppler power spectrum.

The angular power spectrum describes the distribution of the channel power over the spatial angle, for example, the X-axis represents the angle and the Y-axis represents the channel power.

The delay power spectrum describes the distribution of channel power with delay.

The doppler power spectrum describes the distribution of channel power with doppler frequency.

2) And transposition: the new matrix obtained by interchanging the rows and the columns of the matrix A is called a transposed matrix A ^T And is generally indicated by the "right corner mark T". A is m × n type matrix, then transpose matrix A ^T In a n x m matrix.

For example,

3) Conjugate transpose, generally refers to a mathematical transformation of an m x n matrix a, where any element a in the matrix a _ij Belonging to the complex field C.

The symbols of the conjugate transpose correspond to the common transpose "Right-hand symbol T", usually "H Right-hand symbol" is used to represent the conjugate transpose, and the matrix A after conjugate transpose ^H Conjugate transpose matrix called A ^H Is n x m type. For example, first, each element a in A _ij Conjugation is taken to obtainb _ij (the product of two mutually conjugated complex numbers is equal to the square of the modulus of this complex number, the conjugate being generally denoted by the ". Sup.Right-Angle sign"), the new result will be represented by b _ij The new m x n type matrix is marked as a matrix B, and B = A; then, the matrix B is transposed to obtain B ^T I.e. the conjugate transpose matrix of a: b is ^T ＝A ^H 。

4) Vec (·) denotes vectorization operations.

5) Kronecker product (Kronecker product) is an operation between two matrices of arbitrary size, and is expressed as

For example, A is a matrix of m n, B is a matrix of p q,

is a block matrix of mp x nq. For example:

6) Hadamard products, denoted by [, ] indicate that the Hadamard product of matrices a, B is the product of their corresponding positions, and the number of rows and columns of the two matrices are the same, e.g., the multiplication of two m x n matrices.

7) Diag (omega), for constructing a diagonal matrix by placing the column vector omega diagonally, not diagonally, in a square matrix with all 0 elements, e.g.

8) And the statistical covariance matrix is defined as: statistical averaging of the autocorrelation matrix of a random matrix (which may be a column vector).

For example, the statistical covariance matrix of the uplink/downlink channel may be obtained by calculating an autocorrelation matrix for the uplink/downlink channel estimation matrix, and a plurality of autocorrelation matrices may be obtained for a plurality of uplink channel estimation matrices, and a larger number of autocorrelation matrices may be averaged.

Autocorrelation matrix: the matrix is multiplied by the conjugate transpose of the matrix, e.g. the autocorrelation matrix of matrix A is A ^H 。

9) Frequency (frequency) refers to a transmission (e.g., transmission) frequency of a wireless signal, e.g., 1850MHz, 1910MHz.

Bandwidth (bandwidth) refers to frequency bandwidth, e.g., 20MHz, 40MHz. For example, the bandwidth between frequency 1870MHz and frequency 1890MHz is 20MHz.

The frequency band (band) may be regarded as one band from 1850MHz to 1890MHz, or may be divided into multiple bands.

The frequency points are numbered for a fixed frequency, for example, when the frequency interval is 20MHz, the frequency points are divided into the following frequencies from 1850MHz to 1890 MHz: 1850MHz-1870MHz, 1870MHz-1890 MHz, 1890MHz-1910 MHz3 frequency bands, numbering each channel, for example 1, 2, 3 respectively, the fixed frequency numbering is the frequency point.

10 The L2 norm is the modulo square sum of the elements of the vector, then the square root is taken.

11 Semi-positive, a matrix is semi-positive, meaning that the complex quadratic form f (x 1, x2,.. Ang., xn) for the matrix has f (c 1, c2,.. Ang., cn) for any set of complex numbers c1, c2,.. Ang., cn that are not all zero>=0. Alternatively, it means that the matrix A is a conjugate symmetric matrix and has x for any non-zero vector x ^H * A x is more than or equal to 0, and the matrix is called as a semi-positive definite matrix.

12 The dimensions of the matrix described in this application refer to the number of rows and columns of the matrix, for example, when the dimension is a × B, the number of rows and columns of the matrix is a.

For example, dimension M _H M _V X1, the number of rows of the matrix is M _H M _V The number of columns is 1.

For example, dimension M _H M _V M _F X1, the number of rows of the matrix is M _H M _V M _F The number of columns is 1.

For example, dimension M _H M _V M _F M _T X1, the number of rows of the matrix is M _H M _V M _F M _T The number of columns is 1.

For example, dimension M _H ×M _H O _H The number of rows of the matrix is M _H The number of columns is M _H O _H 。

13 Uplink frequency point/uplink frequency and downlink frequency point/downlink frequency in the present application may be frequency points/frequencies belonging to the same frequency band (e.g., 2.1G, 3.5G frequency band), or frequency points/frequencies belonging to different frequency bands (e.g., uplink frequency point belongs to 3.5G frequency band, downlink frequency point belongs to 2.1G frequency band).

14 "and/or" in this application, describe the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The plural in the present application means two or more. In addition, it is to be understood that the terms first, second, etc. in the description of the present application are used for distinguishing between the descriptions and not necessarily for describing a sequential or chronological order.

As shown in fig. 3, a schematic process diagram of a network device determining statistical covariance of a downlink channel based on an uplink channel estimation matrix is introduced.

Step 301: the network device calculates a statistical covariance matrix of the uplink channel based on the uplink channel estimation matrix.

For example, the statistical covariance matrix is a spatial statistical covariance matrix.

For example, the statistical covariance matrix of the uplink channel may be obtained by solving an autocorrelation matrix for the uplink channel estimation matrix, and multiple autocorrelation matrices may be obtained for multiple uplink channel estimation matrices, and a larger number of autocorrelation matrices may be averaged.

Step 302: and the network equipment estimates the angle power spectrum of the channel by using the statistical covariance matrix of the uplink channel.

The angular power spectrum represents the physical characteristics of the channel, describing the distribution of the channel energy with the spatial angle, and the angular power spectrum of the uplink and the downlink is generally considered to be reciprocal.

For example, the angle power spectrum is estimated based on a mapping relationship between a (spatial) statistical covariance matrix of an uplink channel and the angle power spectrum, and in combination with a minimum L2 norm distance criterion.

Step 303: and determining a statistical covariance matrix of the downlink channel by using the angle power spectrum of the channel.

The step utilizes reciprocity of angle power spectrums of an uplink channel and a downlink channel.

Illustratively, the statistical covariance of the downlink channel is determined using a transform matrix (e.g., a Discrete Fourier Transform (DFT) matrix) in which the angular power spectrum corresponds to the downlink channel.

The oversampled DFT matrix is, for example, a spatial oversampled DFT matrix.

The scheme shown in fig. 3 utilizes the relationship between the (spatial) statistical covariance of the uplink channel and the angular power spectrum to obtain the (spatial) statistical covariance matrix of the downlink channel.

In step 302, when estimating the angle power spectrum, the non-negative constraint is not considered, and the estimated angle power spectrum may contain negative elements and have side lobe leakage, which may cause a decrease in the accuracy of the (spatial) statistical covariance of the determined downlink channel. In addition, the scheme shown in fig. 3 can only determine spatial statistical covariance, and cannot determine statistical covariance of other dimensions (e.g., time and frequency), or when the spatial statistical covariance is generalized to statistical covariance in which two or three of the space, the frequency and the time are combined, complexity is greatly increased, and it is difficult to implement.

Based on this, the present application proposes a new method of determining the statistical covariance of the channel. In the new method, the uplink channel estimation matrix is transformed and the average energy of the transformed matrix is counted, followed by determining the power spectrum based on the average energy. And then, based on the determined power spectrum, obtaining a statistical covariance matrix of the downlink channel.

With respect to the example provided above in fig. 3, in this example, instead of finding the statistical covariance of the uplink channel, it is necessary to find (and possibly also store) the statistical mean energy; the power spectrum is estimated by utilizing the relation between the statistical average energy and the power spectrum, but not by utilizing the relation between the statistical covariance and the power spectrum. The estimation method is simple, can be suitable for a scene of solving one or more statistical covariance in a space domain, a frequency domain and a time domain, and is easy to popularize.

The first embodiment is as follows:

as shown in fig. 4, a process diagram of a method for determining a statistical covariance of a downlink channel by a network device is provided.

Step 401: and the network equipment carries out channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix.

Step 402: the network equipment transforms the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to an uplink channel.

Mathematical interpretation: the uplink channel estimation matrix is transformed from a space domain to an angle domain, and/or transformed from a frequency domain to a time delay domain, and/or transformed from a time domain to a Doppler domain.

Step 403: the network equipment determines first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: and performing statistical averaging on the energy corresponding to part or all of the elements in the first channel estimation matrix respectively to obtain the energy.

Step 404: the network device determines a first power spectrum based on the first statistical average energy, wherein a mapping relation exists between the first statistical average energy and the first power spectrum.

Step 405: the network equipment determines a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to a downlink channel.

Subsequently, the network device may transmit data and/or reference signals based on the statistical covariance matrix of the downlink channel.

The method is simple, can accurately determine the statistical covariance of the channel, is more matched with the characteristics of the downlink channel, and is beneficial to improving the performance.

The first related parameter of the embodiment is as follows:

next for step 401: and performing channel estimation based on the received uplink reference signal to obtain a correlation process of an uplink channel estimation matrix for introduction.

The terminal device may periodically transmit the uplink reference signal, and the terminal device may transmit the uplink reference signal by using one or more transmit antennas. The terminal device may send the uplink reference signal at a certain uplink frequency point. The network equipment receives the uplink reference signal from the terminal equipment, and performs channel estimation based on the uplink reference signal to obtain an uplink channel estimation matrix.

When determining the uplink channel estimation matrix, the network device may consider one or more factors of space (e.g., antennas), frequency (e.g., frequency in a bandwidth corresponding to a frequency point), and time (e.g., period).

In an alternative example a, the network device determines the uplink channel estimation matrix considering spatial (antenna) factors. For example, for each transmitting antenna of the terminal device, the network device determines an uplink channel estimation matrix corresponding to the transmitting antenna based on the received uplink reference signal from the transmitting antenna. I.e., one transmit antenna, corresponds to one uplink channel estimation matrix. If the terminal equipment adopts a plurality of transmitting antennas to transmit the uplink reference signals, a plurality of uplink channel estimation matrixes can be determined.

In an alternative example b of the use of,the network equipment considers the frequency factor and determines an uplink channel estimation matrix. For example, the network device performs channel estimation on each resource block, in which case the uplink channel estimation matrix is obtained by combining channel estimation matrices corresponding to a plurality of resource blocks RB, respectively. For example, the total number of resource blocks is M _F ，M _F Is an integer greater than or equal to 1. M th _F The channel estimation matrix corresponding to each resource block is

It can be understood that m _F Is 1 to M _F And t is the time when the network equipment receives the uplink reference signal or is related to the time when the network equipment receives the uplink reference signal. Combining the channel estimation matrixes of all RBs to obtain an uplink channel estimation matrix h _t 。

When M is _F When the value is equal to 1, the reaction solution is,

alternatively, when the frequency factor is not considered, the total number M of resource blocks may be set _F Is regarded as 1, then

When M is _F When the value is more than 1, the uplink channel estimation matrix h _t Channel estimation matrix that can be all RBs

Combinations of (a) and (b). The channel estimation matrix in the upper row is taken as a vector for explanation. For example, the channel estimation matrices of all RBs are spliced into a column vector, satisfying the following equation:

where vec (·) represents a vectorization operation.

In an optional example c, the network device determines considering time and frequency factorsAnd (4) an uplink channel estimation matrix. Regarding the time factor, for example, not only the currently determined uplink channel estimation matrix but also a historical uplink channel estimation matrix may be considered. For example, let time t and the most recent M _T -1 uplink channel estimation matrix at historical time, spliced into a column vector, satisfying the following formula:

wherein, _t hrepresenting the uplink channel estimation matrix, M _T Indicating that the time window used to estimate the doppler power spectrum is long.

In one example, a two-dimensional rectangular antenna array is configured in the network device, and the number of horizontal antennas is M _H The number of vertical antennas is M _V 。

M th _F Channel estimation matrix corresponding to each resource block

Dimension of (D) is, for example, M _H M _V X1, the channel estimation matrix is a column vector, and the ordering mode corresponding to the antennas is as follows: first horizontal and then vertical. Other variations of this dimension are possible, as long as the number of elements in the matrix of the multiple variation dimensions is the same. E.g. dimension is M _H ×M _V Or dimension is M _V ×M _H 。

When the temperature is higher than the set temperature

Time, uplink channel estimation matrix h _t Dimension of (D) is, for example, M _H M _V X1, the uplink channel estimation matrix is a column vector. Or dimension is M _H ×M _V Or dimension is M _V ×M _H 。

When M is _F When the value is more than 1, the uplink channel estimation matrix h _t Is of dimension M _H M _V M _F X1, the uplink channel estimation matrix is a column vector, where M _F To give toTotal number of source blocks. Other variations of this dimension are possible, as long as the number of elements in the matrix of the multiple variation dimensions is the same. E.g. dimension is M _H M _V ×M _F Or dimension is M _H ×M _V M _F 。

When M is _F When the sum is greater than 1, the uplink channel estimation matrix _t hIs of dimension M _H M _V M _F M _T X1, the uplink channel estimation matrix is a column vector. Other variations of this dimension are possible, as long as the number of elements in the matrix of the multiple variation dimensions is the same. E.g. dimension is M _H M _V ×M _F M _T Or dimension is M _H ×M _V M _F M _T . Or dimension is M _H M _V M _F ×M _T 。

The following description is given by taking the channel estimation matrix of the upper row as a column vector.

Next for step 402: a correlation process for transforming the uplink channel estimation matrix based on a first transformation matrix (the first transformation matrix may be one or more) to obtain a first channel estimation matrix is introduced.

When the uplink channel estimation matrix is transformed, one or more first transformation matrices may be used.

The type of the one or more first transform matrices may be Discrete Cosine Transform (DCT) matrices, or hadamard transform matrices, or DFT matrices, or oversampled DFT matrices. It is noted that the type of the plurality of first transformation matrices is typically the same for these several types.

When the type of the first transform matrix is a Discrete Cosine Transform (DCT) matrix, the first transform matrix is referred to as a first DCT matrix. When the type of the first transform matrix is a hadamard transform matrix, the first transform matrix is referred to as a first hadamard transform matrix. When the type of the first transform matrix is a discrete fourier transform DFT matrix, the first transform matrix is referred to as a first discrete fourier transform DFT matrix. When the type of the first transform matrix is an oversampled DFT matrix, the first transform matrix is referred to as a first oversampled DFT matrix.

In addition, one first transformation matrix may be obtained based on any one of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The at least one first transformation matrix may be obtained based on at least one type of matrix: a spatial domain transform matrix, a frequency domain transform matrix, a time domain transform matrix. A transformation matrix used to determine a spatial domain type of the first transformation matrix is referred to as a first spatial domain transformation matrix, a transformation matrix used to determine a frequency domain type of the first transformation matrix is referred to as a first frequency domain transformation matrix, and a transformation matrix used to determine a time domain type of the first transformation matrix is referred to as a first time domain transformation matrix.

It is to be understood that, when the type of the at least one first transformation matrix is a Discrete Cosine Transformation (DCT) matrix and the at least one first transformation matrix is obtained based on at least one type of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, the at least one first transformation matrix may be regarded as being obtained based on at least one type of a spatial domain DCT matrix, a frequency domain DCT matrix, and a time domain DCT matrix. When the type of the at least one first transform matrix is an oversampled DFT matrix and the at least one first transform matrix is obtained based on at least one type of a spatial domain transform matrix, a frequency domain transform matrix, and a time domain transform matrix, the at least one first transform matrix may be regarded as being obtained based on at least one type of a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. The other types of matrices are similar and will not be repeated.

Optionally, the spatial domain matrix may be further divided into a spatial domain horizontal matrix and a spatial domain vertical matrix.

A first transformation matrix obtained based on the spatial domain horizontal matrix is recorded as F _H Dimension being, for example, M _H ×M _H O _H ，O _H Oversampling factor, M, representing the spatial domain level _H Indicating the number of horizontal antennas. As can be appreciated, dimensionThe degree may have other variations, e.g. dimension M _H O _H ×M _H 。

A first transformation matrix obtained based on the spatial domain vertical matrix is recorded as F _V Dimension being, for example, M _V ×M _V O _V ，O _V Representing spatial domain vertical oversampling multiple, M _V Indicating the number of vertical antennas. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _V O _V ×M _V 。

Denote a first transformation matrix obtained based on the frequency domain matrix as F _F Dimension is for example M _F ×M _F O _F ，O _F Representing the oversampling multiple, M, of the frequency domain _F Representing the total number of resource blocks. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _F O _F ×M _F 。

Let a first transformation matrix obtained based on the time domain matrix be FT, with a dimension of M for example _T ×M _T O _T ，O _T Representing the oversampling multiple, M, of the time domain _T Indicating that the time window used to estimate the doppler power spectrum is long. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _T O _T ×M _T 。

In addition, when there is no oversampling, for example, none of the DCT matrix, the DFT matrix, and the Hadamard transform matrix has an operation of oversampling, in which case O _H 、O _V 、O _F 、O _T May each be 1.

It is noted that the first transformation matrix (e.g., F) is introduced here _H ，F _V ，F _F ，F _T ) The first transformation matrix is used for transforming the uplink channel estimation matrix, and the first transformation matrix corresponds to the uplink. A second transformation matrix is also introduced below (e.g.,

) And the second transformation matrix corresponds to the downlink and is used for determining a statistical covariance matrix of a downlink channel.

Optionally, F _H ，F _V ，F _F ，F _T Each matrix in (a) satisfies the following condition: the L2 norm of each column of the matrix is 1, and it is understood that the L2 norm of a column vector means that the sum of the squares of the elements in the column vector is squared and then the square root is equal to 1.

Exemplary, F _H ，F _V ，F _F ，F _T The following formula is satisfied. Let F denote a matrix of dimension M × MO, with the M-th row and n-th column elements:

wherein M is an integer from 1 to M, and n is an integer from 1 to M O. Wherein M may correspond to M introduced above _H 、M _V 、M _F 、M _T (ii) a O may correspond to O introduced above _H 、O _V 、O _F 、O _T . For example, the formula is applied to the generator matrix F _H When M in the formula is M _H O in the formula is O _H 。F _V ，F _F ，F _T Similarly, they will not be described one by one.

And transforming the uplink channel estimation matrix based on the first transformation matrix to obtain a first channel estimation matrix:

for example: and multiplying the one or more first transformation matrixes by the uplink channel estimation matrix to obtain a first channel estimation matrix.

E.g. based on one or more first transformation matrices, and kronecker shift

And one or more algorithms such as transposition, conjugate transposition and the like are used for obtaining the first channel estimation matrix.

For example, kronecker shift of a plurality of first transformation matrices

Multiplying the uplink channel estimation matrix to obtain a first channel estimation momentAnd (5) arraying.

For example, the kronecker product of a plurality of first transformation matrices

And the conjugate transpose of the obtained matrix is multiplied by the uplink channel estimation matrix to obtain a first channel estimation matrix.

In an alternative example a, the first channel estimation matrix satisfies the following equation:

for example, F _H Dimension of M _H ×M _H O _H ，F _V Dimension of M _V ×M _V O _V ，F _F Dimension of M _F ×M _F O _F ，F _T Dimension of M _T ×M _T O _T ， _t hDimension is M _H M _V M _F M _T ×1，g _t Is of dimension M _H M _V M _F M _T O _H O _V O _F O _T ×1。

In an alternative example b, the first channel estimation matrix satisfies the following equation:

for example, F _H Dimension of M _H ×M _H O _H ，F _V Dimension of M _V ×M _V O _V ，F _F Dimension of M _F ×M _F O _F ，h _t Dimension is M _H M _V M _F ×1，g _t Dimension is M _H M _V M _F O _H O _V O _F ×1。

In an alternative example c, the first channel estimation matrix satisfies the following equation:

for example, F _H Dimension of M _H ×M _H O _H ，F _V Dimension of M _V ×M _V O _V ，h _t Dimension is M _H M _V ×1，

Is of dimension M _H M _V O _H O _V X 1. Wherein,

optionally, the present application may also apply F _H ，F _V ，F _F ，F _T Kronecker product of a plurality of these 4 matrices

Is considered a first transformation matrix. For example, the first transformation matrix is

Or the first transformation matrix is

Or the first transformation matrix is

Optionally, the application can also use F _H ，F _V ，F _F ，F _T The conjugate transpose of the matrix resulting from the kronecker product of the 4 matrices is considered to be the first transformation matrix, e.g., the first transformation matrix is

Or the first transformation matrix is

Or the first transformation matrix is

Where H denotes a conjugate transpose.

The uplink channel estimation matrix is transformed, and if the transformed channel estimation matrix is sparse (for example, 100 × 1 vector, only 10 elements have large values after transformation, and other elements have values close to 0, and elements close to 0 can be filtered out), the channel estimation matrix can be regarded as being compressed, so that the storage overhead can be reduced.

Next, for step 403: a correlation process is introduced to determine a first statistically averaged energy corresponding to the first channel estimate matrix(s).

The first statistical average energy is: and performing statistical averaging on energy corresponding to part or all elements in one or more first channel estimation matrixes respectively to obtain the energy. For example, it may be based on Adama product &, conjugate (.) ^* Determining energy corresponding to part or all elements in the first channel estimation matrix respectively in a calculation mode; the energy of an element may be statistically averaged based on the expected E.

Here, the plurality of first uplink channel estimation matrices may be obtained based on one or more factors of a plurality of transmit antennas, a plurality of frequencies, a plurality of periods, and the like. For example, one first uplink channel estimation matrix corresponding to one transmit antenna is one first uplink channel estimation matrix corresponding to a plurality of transmit antennas. For example, a first uplink channel estimation matrix corresponding to one frequency is a first uplink channel estimation matrix corresponding to a plurality of frequencies. For example, one uplink channel estimation matrix is determined in one period, and a plurality of uplink channel estimation matrices are determined in a plurality of periods.

In an alternative example, the first statistical average energy satisfies the following equation:

wherein gt is a first channel estimate matrix, phi indicates a first statistical average energy, E indicates an expectation that an expectation can be obtained by statistically averaging one or more first channel estimate matrices to an average of:, | _ indicates an hadamard product indicating a product of corresponding positions of two matrices, (.) ^* Representing the conjugation, each element a in the matrix gt _ij Conjugation is taken to obtain b _ij (the product of two mutually conjugated complex numbers is equal to the square of the modulus of this complex number, the conjugation being usually denoted by the ". Left-right index"), the new result will be represented by b _ij The new matrix of composition is recorded as matrix

It can be understood that g _t Can also be replaced by

In the statistical averaging, the statistical averaging may be performed for one or more factors of different time, different transmitting antennas, different frequencies, and the like. One transmitting antenna of the terminal equipment corresponds to one first channel estimation matrix, and one transmitting antenna corresponds to one first channel estimation matrix

Can be paired with

Statistically averaged over time with different transmit antennas of the terminal device, e.g. over a plurality of transmit antennas acquired over different time and different transmit antennas

Statistical averaging is performed. E.g. for a plurality of signals obtained at different times and different frequencies

Statistical averaging is performed.

In one example, the first channel estimation matrix is a column vector, for exampleSuch as g _t Is of dimension M _H M _V M _F MTO _H O _V O _F O _T X1, or M _H M _V M _F X1, or M _H M _V O _H O _V X 1. Correspondingly, the first statistical mean energy is a column vector, and the dimension of the first statistical mean energy is, for example, M _H M _V M _F M _T O _H O _V O _F O _T X1, or M _H M _V M _F X1, or M _H M _V O _H O _V ×1。

Next for step 404: a correlation process for determining a first power spectrum is introduced based on the first statistically averaged energy.

A mapping relationship exists between the first statistical average energy and the first power spectrum, and the mapping relationship satisfies the following formula:

Tω＝φ，

where ω is the first power spectrum, φ is the first statistical average energy, T is a mapping matrix, and T is associated with the first transformation matrix.

In an alternative example, the first power spectrum is a column vector.

It will be appreciated that the first channel estimation matrix is based on F _H ，F _V ，F _F ，F _T And the mapping matrix T is also derived based on these matrices. In addition, the first power spectrum also represents a corresponding power spectrum, and the first power spectrum may be a combination of one or more of an angle power spectrum, a time delay power spectrum, and a doppler power spectrum. The angle power spectrum corresponds to a space domain, the delay power spectrum corresponds to a frequency domain, and the Doppler power spectrum corresponds to a time domain.

For example, when the first transformation matrix is based on a spatial domain matrix (e.g., F) _H ，F _V ) When obtained, the first power spectrum is an angular power spectrum.

For example, when the first transformation matrix is based on a frequency domain matrix (e.g., F) _F ) When obtained, the first power spectrum is a time delay power spectrum.

For example, when the first transformation matrix is based on a time domain matrix (e.g., F) _T ) When obtained, the first power spectrum is a doppler power spectrum.

For example, when the first transformation matrix is based on a spatial domain matrix, a frequency domain matrix (e.g., F) _H ，F _V ，F _F ) When obtained, the first power spectrum is a combination of an angle power spectrum and a time delay power spectrum.

For example, when the first transformation matrix is based on a spatial domain matrix, a frequency domain matrix, a time domain matrix (e.g., F) _H ，F _V ，F _F ，F _T ) When obtained, the first power spectrum is a combination of an angle power spectrum, a delay power spectrum and a doppler power spectrum.

And when the first power spectrum is a power spectrum combined by an angle power spectrum, a time delay power spectrum and a Doppler power spectrum, the statistical covariance matrix of the subsequently determined downlink channel is a space, frequency and time combined statistical covariance matrix.

In an alternative example, each element in the first power spectrum is a non-negative real value. Therefore, the semi-positive determination of the determined statistical covariance matrix of the downlink channel can be ensured, and the accuracy of the determined statistical covariance matrix of the downlink channel can be improved. This can solve the problem in fig. 3 that it cannot be guaranteed that the power spectrum is not negative.

The mapping matrix T being related to said first transformation matrix, e.g. mapping matrices T and F _H ，F _V ，F _F ，F _T Is correlated with one or more matrices in the array. For example, the mapping matrix T is based on F _H ，F _V ，F _F ，F _T And based on conjugation, conjugate transpose, hadamard-product-all-inclusive, and kronecker-product

Is determined by one or more algorithms.

In an alternative example a of the method,

in an alternative example b of the method,

in an alternative example c of the use of the solution,

in an alternative example of this, the first,

in one alternative example of this, the user may,

in an alternative example of this, the first,

in an alternative example of this, the first,

in this application, a minimum L2-norm distance criterion, or a minimum KL divergence criterion, or a minimum L0-norm criterion may be employed to determine the first power spectrum based on the first statistically averaged energy (e.g., estimate ω based on T and Φ).

In the three examples provided below, the constraint ω ≧ 0 indicates that each element in ω is non-negative.

In example 1, when the minimum L2 norm distance criterion is adopted, the following optimization problem can be modeled:

s.t.ω≥0

the optimization problem is a standard non-negative least square (NNLS) problem, and can be solved by using an existing NNLS algorithm.

In one example 2, when the minimum KL divergence criterion is employed, the following optimization problem can be modeled:

s.t.ω≥0

to avoid the constraint of ω ≧ 0, let λ denote the root mean square of the elements of ω, i.e., ω = λ ≧ λ, one can obtain:

the objective function is derived for λ and the derivative is made zero, which yields:

T ^T q⊙λ-T ^T 1⊙λ＝0；

a column vector in which all elements represented by 1 are all 1, the dimension of the column vector being, for example, M _H M _V M _F M _T O _H O _V O _F O _T X1, or M _H M _V M _F O _H O _V O _F X1, or M _H M _V O _H O _V X1 or other dimensions. And, instead,

the following iterative process is therefore constructed:

for n＝0：N _Iter

if n＝＝0

else

end

wherein for can be understood as "loop execution", and N is from 0 to N _Iter 。N _Iter The number of iterations is indicated and is,

the pseudo-inverse is calculated, and max (a, b) is the maximum value of a and b. Output after iteration is completed

In one example 3, when the minimum L0 norm criterion is employed, it can be modeled as an optimization problem as follows:

s.t.Tω＝φ

ω≥0

the Matching Pursuit (MP) algorithm can be used for solving, and the specific flow is as follows:

step 1: find the largest term in phi

Record its corresponding position n _max And adds it to the recovered angular delay doppler power spectrum ω:

the initial value of ω is a zero vector.

And 2, step: subtracting phi from phi

If an element of phi after the subtractive cancellation is less than zero, the element is addedSetting to zero:

and step 3: power correction is performed on each element of ω found previously:

and 4, step 4: and repeating the three steps until the maximum element in phi is smaller than a preset threshold or the cycle number reaches a preset maximum value. The power threshold is typically set to be found for the first time

The maximum number of cycles is typically 50% of 1%, but other values are possible, such as 40, or 30, or 60, for example.

Next for step 405: and determining a correlation process of a statistical covariance matrix of the downlink channel for introduction based on the first power spectrum and the second transformation matrix.

When determining the statistical covariance matrix of the downlink channel, the second transformation matrix may be one or more.

The type of the one or more second transform matrices may be discrete cosine transform, DCT, or hadamard transform matrices, or DFT matrices, or oversampled DFT matrices. It is noted that the type of the plurality of second transformation matrices is generally the same for these several types. For these types, the types of the first transformation matrix and the second transformation matrix may be the same or different.

When the type of the second transform matrix is a Discrete Cosine Transform (DCT) matrix, the second transform matrix is referred to as a second DCT matrix. When the type of the second transform matrix is a hadamard transform matrix, the second transform matrix is referred to as a second hadamard transform matrix. When the type of the second transform matrix is a discrete fourier transform DFT matrix, the second transform matrix is referred to as a second discrete fourier transform DFT matrix. When the type of the second transform matrix is an oversampled DFT matrix, the second transform matrix is referred to as a second oversampled DFT matrix.

In addition, a second transformation matrix may be obtained based on any one of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The at least one second transformation matrix may be obtained based on at least one type of matrix: a spatial domain transform matrix, a frequency domain transform matrix, a time domain transform matrix. A transformation matrix used to determine the spatial domain type of the second transformation matrix is referred to as a second spatial domain transformation matrix, a transformation matrix used to determine the frequency domain type of the second transformation matrix is referred to as a second frequency domain transformation matrix, and a transformation matrix used to determine the time domain type of the second transformation matrix is referred to as a second time domain transformation matrix.

It is to be understood that, when the type of the at least one second transformation matrix is a Discrete Cosine Transformation (DCT) matrix and the at least one second transformation matrix is obtained based on at least one type of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, the at least one second transformation matrix may be regarded as being obtained based on at least one type of a spatial domain DCT matrix, a frequency domain DCT matrix, and a time domain DCT matrix. When the type of the at least one second transform matrix is an oversampled DFT matrix and the at least one second transform matrix is obtained based on at least one type of a spatial domain transform matrix, a frequency domain transform matrix, and a time domain transform matrix, the at least one second transform matrix may be regarded as being obtained based on at least one type of a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. The other types of matrices are similar and will not be repeated.

As for the types of matrices mentioned above, the discrete cosine transform DCT matrix, or the hadamard transform matrix, or the DFT matrix, or the oversampled DFT matrix, the second transform matrix may be of the same type as or different from the first transform matrix, for example, the first transform matrix is a DCT matrix and the second transform matrix is a hadamard transform matrix. When the types of the first transformation matrix and the second transformation matrix are the same, the specific contents of the first transformation matrix and the second transformation matrix may be the same or different. It should be noted that, the three types of spatial domain, frequency domain and time domain, the first transformation matrix and the second transformation matrix are the same, for example, the first transformation matrix is obtained based on the spatial domain transformation matrix, and the second transformation matrix is also obtained based on the spatial domain transformation matrix, or the first transformation matrix is 2, and is obtained based on the frequency domain transformation matrix and the time domain transformation matrix, respectively, and the second transformation matrix is also 2, and is obtained based on the frequency domain transformation matrix and the time domain transformation matrix, respectively.

Recording a second transformation matrix obtained based on the spatial domain horizontal matrix as

Dimension of M _H ×M _H O _H ，O _H Over-sampling multiple, M, at the spatial domain level _H Indicating the number of horizontal antennas.

Recording a second transformation matrix obtained based on the spatial domain vertical matrix as

Dimension of M _V ×M _V O _V ，O _V Over-sampling multiple, M, representing the spatial domain verticality _V Indicating the number of vertical antennas.

Denote a second transformation matrix obtained based on the frequency domain matrix as

Dimension of M _F ×M _F O _F ，O _F Representing spatial domain vertical oversampling multiple, M _F Representing the total number of resource blocks.

Recording a second transformation matrix obtained based on the time domain matrix as

Dimension of M _T ×M _T O _T ，O _T Representing spatial domain vertical oversampling multiple, M _T Indicating the length of the time window used to estimate the doppler power spectrum.

In addition, when there is no oversampling, for example, none of the DCT matrix, DFT matrix, and Hadamard transform matrix has an operation of oversampling, in which case O _H 、O _V 、O _F 、O _T May each be 1.

Optionally, the number and dimensions of the second transformation matrix are the same as those of the first transformation matrix.

A first transformation matrix (e.g., F) is introduced above _H ，F _V ，F _F ，F _T ) The first transformation matrix is used for transforming the uplink channel estimation matrix, and the first transformation matrix corresponds to the uplink. The second transformation matrix (e.g.,

) And corresponding to the downlink, the method is used for determining the statistical covariance matrix of the downlink channel.

In the alternative,

each matrix in (a) satisfies the following condition: the L2 norm of each column of the matrix is 1.

Exemplaryly,

the following formula is satisfied. Let F denote a matrix of dimension M × MO, whose M-th row and n-th column have the elements:

wherein M is an integer from 1 to M, and n is an integer from 1 to M O. Wherein M may correspond to M introduced above _H 、M _V 、M _F 、M _T (ii) a O may correspond to O introduced above _H 、O _V 、O _F 、O _T . For example,applying the formula to the generator matrix

Then M in the formula is M _H O in the formula is O _H 。

Similarly, they will not be described one by one.

When determining the statistical covariance matrix of the downlink channel based on the first power spectrum and the second transformation matrix:

e.g., based on one or more second transformation matrices, a first power spectrum, and a kronecker product

And performing one or more algorithms of transposition, conjugate transposition and diag to obtain a statistical covariance matrix of the downlink channel.

And multiplying by diag (omega) to obtain a statistical covariance matrix of the downlink channel.

Where ω is the first power spectrum, e.g., the first power spectrum is a column vector, and diag (ω) is used to indicate that the column vector is placed on a diagonal, e.g., the diagonal

As another example, the kronecker product of the plurality of first transformation matrices

And obtaining a matrix, multiplying the matrix by diag (omega), and multiplying by the conjugate transpose of the matrix to obtain the statistical covariance of the downlink channel.

In an optional example a, the statistical covariance matrix R of the downlink channel satisfies the following formula:

for example, the statistical covariance matrix is obtained from transformation matrices respectively obtained based on a spatial domain, a frequency domain and a time domain, and the statistical covariance matrix may be referred to as spatial frequency time joint statistical covariance.

In an optional example b, the statistical covariance matrix of the downlink channel satisfies the following formula:

for example, the statistical covariance matrix is obtained according to transformation matrices respectively obtained based on a spatial domain and a frequency domain, and the statistical covariance may be referred to as spatial-frequency joint statistical covariance.

In an optional example c, the statistical covariance matrix of the downlink channel satisfies the following formula:

illustratively, the statistical covariance matrix is derived from a transformation matrix obtained based on the spatial domain, and the statistical covariance may be referred to as spatial statistical covariance.

The method comprises the steps that an uplink channel estimation matrix is transformed through a first transformation matrix (such as a DFT matrix/an oversampling DFT matrix) of a space domain, a frequency domain and a time domain to obtain a first channel estimation matrix; obtaining a first statistical average energy based on the first channel estimation matrix; estimating to obtain an angle, a time delay and a Doppler power spectrum by utilizing a mapping relation between the first statistical average energy and the angle, the time delay and the Doppler power spectrum; and finally, reconstructing the spatial, frequency and time joint statistical covariance of the downlink channel based on a second transformation matrix (such as a DFT matrix/an oversampling DFT matrix) of a spatial domain, a frequency domain and a time domain corresponding to the angle, the time delay and the Doppler power spectrum and the downlink. Optionally, the angle, the delay, and the doppler power spectrum are estimated by using a mapping relationship between the first statistical average energy and the angle, the delay, and the doppler power spectrum, and combining a criterion under a non-negative constraint (for example, a minimum L2 norm distance criterion, a minimum KL divergence criterion, or a minimum L0 norm criterion).

The first embodiment above describes a process of determining, by a network device, a statistical covariance of a downlink channel in order to transmit a downlink reference signal and/or downlink data. In another embodiment of the present application, the terminal device also determines the statistical covariance of the uplink channel by using a similar method, so as to transmit the uplink reference signal and/or the uplink data.

Example two:

the process of the method shown in the second embodiment is similar to that of the method shown in the first embodiment, and the uplink and the downlink are reversed. Correspondingly, the terminal device in the first embodiment may be changed to a network device, the network device in the first embodiment may be changed to a terminal device, the uplink channel estimation matrix in the first embodiment may be changed to a downlink channel estimation matrix, and the statistical covariance matrix of the downlink channel in the first embodiment may be changed to a statistical covariance matrix of the uplink channel.

In addition, the names of partial nouns may be modified to distinguish them. For example, the first transformation matrix is changed into a third transformation matrix, and the third transformation matrix is related to a downlink channel; changing the first channel estimation matrix to a second channel estimation matrix; and changing the first statistical average energy into a second statistical average energy, changing the first power spectrum into a second power spectrum, and changing the second transformation matrix into a fourth transformation matrix, wherein the fourth transformation matrix is related to an uplink channel.

As shown in fig. 5, a flow of communication based on statistical covariance of an uplink channel is introduced.

Some or all of the antenna(s) on the network device transmit downlink reference signals to the terminal device. The terminal equipment carries out channel estimation based on the received downlink reference signals, and estimates a downlink channel estimation matrix of a channel between each transmitting antenna of the network equipment and the terminal equipment. The downlink channel estimation matrix may be a matrix or a vector (vector is a one-dimensional matrix).

Then, the terminal device determines a statistical covariance matrix of the uplink channel based on the downlink channel estimation matrix.

The statistical covariance matrix of the uplink channel may be used for uplink pilot weighting, and then the uplink reference signal is sent. In addition, the statistical covariance matrix of the uplink channel can be used for single-user weight calculation, precoding and the like, and then uplink data is sent.

As shown in fig. 6, a process diagram of a method for determining a statistical covariance of an uplink channel by a terminal device is provided.

Step 601: and the terminal equipment carries out channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix.

Step 602: the terminal equipment transforms the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to a downlink channel.

Step 603: the terminal equipment determines second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: and performing statistical averaging on the energy corresponding to part or all of the elements in the second channel estimation matrix to obtain the energy.

Step 604: and the terminal equipment determines a second power spectrum based on the second statistical average energy, wherein a mapping relation exists between the second statistical average energy and the second power spectrum.

Step 605: the terminal equipment determines a statistical covariance matrix of an uplink channel based on the second power spectrum and the fourth transformation matrix; the fourth transformation matrix is a matrix related to an uplink channel.

Subsequently, the terminal device may transmit data and/or reference signals based on the statistical covariance matrix of the uplink channel.

The parameters related to the second embodiment are as follows:

the difference between the above parameters and the first embodiment includes: m is a group of _H 、M _V Representing the number of antennas in the terminal device, but not in the network device, F _H ，F _V ，F _F ，F _T Corresponding to the downlink, the downlink is sent to the mobile terminal,

corresponding to the uplink, h _t 、 _t hAnd R corresponds to the downlink.

Next, for step 601: the terminal device performs channel estimation based on the received downlink reference signal, and introduces a correlation process of obtaining a downlink channel estimation matrix.

The network device may periodically transmit the downlink reference signal, and the network device may transmit the downlink reference signal using one or more transmit antennas. The network device may transmit a downlink reference signal at a certain downlink frequency point. The terminal equipment receives a downlink reference signal from the network equipment, and performs channel estimation based on the downlink reference signal to obtain a downlink channel estimation matrix.

The terminal device may consider one or more factors of space (e.g., antennas), frequency (e.g., frequency in a bandwidth corresponding to a frequency point), and time (e.g., period) when determining the downlink channel estimation matrix.

In an alternative example a, the terminal device determines the downlink channel estimation matrix considering spatial (antenna) factors. For example, for each transmitting antenna of the network device, the terminal device determines a downlink channel estimation matrix corresponding to the transmitting antenna based on the received downlink reference signal from the transmitting antenna. I.e., one transmit antenna, corresponds to one downlink channel estimation matrix. If the network device uses multiple transmit antennas to transmit downlink reference signals, multiple downlink channel estimation matrices can be determined.

In an optional example b, the terminal device determines the downlink channel estimation matrix in consideration of frequency factors. For example, the terminal device performs channel estimation on each resource block, and in this case, the downlink channel estimation matrix is obtained by combining channel estimation matrices corresponding to a plurality of resource blocks RB, respectively. For example, the total number of resource blocks is M _F ，M _F Is an integer greater than or equal to 1. M th _F The channel estimation matrix corresponding to each resource block is

It can be understood that m _F Is 1 to M _F And t is the time when the terminal equipment receives the downlink reference signal or is related to the time when the terminal equipment receives the downlink reference signal. And combining the channel estimation matrixes of all RBs to obtain a downlink channel estimation matrix ht.

When M is _F When the value is equal to 1, the reaction solution is,

When M is _F When the value is more than 1, the downlink channel estimation matrix h _t Channel estimation matrix which may be all RBs

Combinations of (a) and (b). The following channel estimation matrix is taken as a vector for explanation. For example, the channel estimation matrices of all RBs are spliced into a column vector, which satisfies the following formula:

where vec (·) represents a vectorization operation.

In an optional example c, the terminal device determines the downlink channel estimation matrix in consideration of time and frequency factors. Regarding the time factor, for example, not only the currently determined downlink channel estimation matrix but also a historical downlink channel estimation matrix may be considered. For example, the downlink channel estimation matrices at time t and the latest MT-1 historical times are spliced into a column vector, which satisfies the following formula:

wherein, _t hrepresenting a downlink channel estimation matrix, M _T Indicating that the time window used to estimate the doppler power spectrum is long.

In one example, a two-dimensional rectangular antenna array is configured in the terminal equipment, and the number of horizontal antennas is M _H Number of vertical antennas is M _V 。

M th _F Channel estimation matrix corresponding to each resource block

When in use

Time, downlink channel estimation matrix h _t Dimension of (D) is, for example, M _H M _V X1, the downlink channel estimation matrix is a column vector. Or dimension is M _H ×M _V Or dimension is M _V ×M _H 。

When M is _F When the value is more than 1, the downlink channel estimation matrix h _t Is of dimension M _H M _V M _F X1, the downlink channel estimation matrix is a column vector, wherein M _F Is the total number of resource blocks. Other variations of this dimension are possible, as long as the number of elements in the matrix of the multiple variation dimensions is the same. E.g. dimension is M _H M _V ×M _F Or dimension is M _H ×M _V M _F 。

When M is _F When the sum is more than 1, the downlink channel estimation matrix _t hIs of dimension M _H M _V M _F M _T X1, the downlink channel estimation matrix is a column vector. Other variations of this dimension are possible, as long as the number of elements in the matrix of the multiple variation dimensions is the same. E.g. dimension is M _H M _V ×M _F M _T Or dimension is M _H ×M _V M _F M _T . Or dimension is M _H M _V M _F ×M _T 。

The following description will take the column vector as an example of the channel estimation matrix of the following row.

Next for step 602: a correlation process of transforming the downlink channel estimation matrix based on a third transformation matrix (the third transformation matrix may be one or more) to obtain a second channel estimation matrix is introduced.

When the downlink channel estimation matrix is transformed, one or more third transformation matrices may be used.

The type of the one or more third transform matrices may be Discrete Cosine Transform (DCT) matrices, or hadamard transform matrices, or DFT matrices, or oversampled DFT matrices. It is noted that the types of the plurality of third transformation matrices are generally the same for these several types.

When the type of the third transform matrix is a Discrete Cosine Transform (DCT) matrix, the third transform matrix is referred to as a third DCT matrix. When the type of the third transform matrix is a hadamard transform matrix, the third transform matrix is referred to as a third hadamard transform matrix. When the type of the third transform matrix is a discrete fourier transform, DFT, matrix, the third transform matrix is referred to as a third discrete fourier transform, DFT, matrix. When the type of the third transform matrix is an oversampled DFT matrix, the third transform matrix is referred to as a third oversampled DFT matrix.

In addition, a third transformation matrix may be obtained based on any one of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The at least one third transformation matrix may be obtained based on at least one type of matrix: a spatial domain transform matrix, a frequency domain transform matrix, a time domain transform matrix. A transform matrix used to determine a spatial domain type of the third transform matrix is referred to as a third spatial domain transform matrix, a transform matrix used to determine a frequency domain type of the third transform matrix is referred to as a third frequency domain transform matrix, and a transform matrix used to determine a time domain type of the third transform matrix is referred to as a third time domain transform matrix.

It is to be understood that, when the type of the at least one third transformation matrix is a discrete cosine transformation DCT matrix, and the at least one third transformation matrix is obtained based on at least one type of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, the at least one third transformation matrix may be regarded as being obtained based on at least one type of a spatial domain DCT matrix, a frequency domain DCT matrix, and a time domain DCT matrix. When the type of the at least one third transform matrix is an oversampled DFT matrix and the at least one third transform matrix is obtained based on at least one type of a spatial domain transform matrix, a frequency domain transform matrix, and a time domain transform matrix, the at least one third transform matrix may be regarded as being obtained based on at least one type of a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. The other types of matrices are similar and will not be repeated.

The third transformation matrix obtained based on the spatial domain horizontal matrix is recorded as F _H Dimension being, for example, M _H ×M _H O _H ，O _H Oversampling factor, M, representing the spatial domain level _H Indicating the number of horizontal antennas. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _H O _H ×M _H 。

Let the third transformation matrix obtained based on the spatial domain vertical matrix be F _V Dimension is for example M _V ×M _V O _V ，O _V Representing spatial domain vertical oversampling multiple, M _V Indicating the number of vertical antennas. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _V O _V ×M _V 。

Let the third transformation matrix obtained based on the frequency domain matrix be F _F Dimension is for example M _F ×M _F O _F ，O _F Denotes the oversampling multiple, M, of the frequency domain _F Representing the total number of resource blocks. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _F O _F ×M _F 。

Let a third transformation matrix obtained based on the time domain matrix be denoted as FT, with a dimension of M for example _T ×M _T O _T ，O _T Representing the oversampling multiple, M, of the time domain _T Indicating that the time window used to estimate the doppler power spectrum is long. It will be appreciated that other variations of the dimension are possible, for example, a dimension of M _T O _T ×M _T 。

In addition, when there is no oversampling, for example, none of the DCT matrix, DFT matrix, and Hadamard transform matrix has an operation of oversampling, in which case O _H 、O _V 、O _F 、O _T May be 1.

It should be noted that the third transformation matrix (e.g., F) is introduced here _H ，F _V ，F _F ，F _T ) Is used for transforming the downlink channel estimation matrix, and the third transformation matrix corresponds to the downlink channel estimation matrix. A fourth transformation matrix is also introduced below (e.g.,

) And the fourth transformation matrix corresponds to an uplink and is used for determining a statistical covariance matrix of an uplink channel.

Optionally, F _H ，F _V ，F _F ，F _T Each matrix in (a) satisfies the following condition: the L2 norm of each column of the matrix is 1, and it is understood that the L2 norm of a column vector means the sum of the squares of the elements in the column vector, and then the square root is equal to 1.

Exemplary, F _H ，F _V ，F _F ，F _T The following formula is satisfied. Let F denote a matrix of dimension M × MO, whose M-th row and n-th column have the elements:

wherein M is an integer from 1 to M, and n is an integer from 1 to M O. Wherein M may correspond to M introduced above _H 、M _V 、M _F 、M _T (ii) a O may correspond to O introduced above _H 、O _V 、O _F 、O _T . For example, the formula is applied to the generator matrix F _H Then M in the formula is M _H O in the formula is O _H 。F _V ，F _F ，F _T Similarly, they will not be described one by one.

And transforming the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix:

for example: and multiplying the one or more third transformation matrixes by the downlink channel estimation matrix to obtain a second channel estimation matrix.

E.g. based on one or more third transformation matrices, and a kronecker product

Transposing, conjugate transposing and the like to obtain one or more algorithmsTo a second channel estimation matrix.

For example, the kronecker product of a plurality of third transformation matrices

And multiplying the downlink channel estimation matrix to obtain a second channel estimation matrix.

And the conjugate transpose of the obtained matrix is multiplied by the downlink channel estimation matrix to obtain a second channel estimation matrix.

In an alternative example a, the second channel estimation matrix satisfies the following equation:

In an alternative example b, the second channel estimation matrix satisfies the following equation:

In an alternative example c, the second channel estimation matrix satisfies the following equation:

Is of dimension M _H M _V O _H O _V X 1. Wherein,

optionally, the present application may also apply F _H ，F _V ，F _F ，F _T Kronecker product of a plurality of the 4 matrices

Is considered a third transformation matrix. For example, the third transformation matrix is

Or the third transformation matrix is

Or the third transformation matrix is

Optionally, the present application may also apply F _H ，F _V ，F _F ，F _T The conjugate transpose of the matrix obtained by the kronecker product of the 4 matrices is regarded as the third matrixTransformation matrices, e.g. third transformation matrix of

Or the third transformation matrix is

Or the third transformation matrix is

Where H denotes a conjugate transpose.

Next for step 603: a correlation process is introduced that determines a second statistical average energy corresponding to the second channel estimate matrix(s).

The second statistical mean energy is: and performing statistical averaging on the energy corresponding to part or all of the elements in the one or more second channel estimation matrixes to obtain the energy. For example, it may be based on Adama product &, conjugate (.) ^* Determining energy corresponding to part or all elements in the second channel estimation matrix respectively in a calculation mode; the energy of an element may be statistically averaged based on the expected E.

The plurality of first downlink channel estimation matrices may be derived based on one or more factors of a plurality of transmit antennas, a plurality of frequencies, a plurality of periods, and the like. For example, a first downlink channel estimation matrix corresponding to one transmit antenna, and a first downlink channel estimation matrix corresponding to multiple transmit antennas. For example, a first downlink channel estimation matrix for one frequency, and a first downlink channel estimation matrix for multiple frequencies. For example, one downlink channel estimation matrix is determined for one period, and a plurality of downlink channel estimation matrices are determined for a plurality of periods.

In an alternative example, the second statistical mean energy satisfies the following equation:

wherein gt is a second channel estimation matrix,phi denotes a second statistical average energy, E denotes an expectation that the expectation can be obtained by statistically averaging one or more second channel estimation matrices, indicates a hadamard product indicating a product of corresponding positions of two matrices, (-) ^* Representing the conjugation, each element a in the matrix gt _ij Conjugation is taken to obtain b _ij (the product of two mutually conjugated complex numbers is equal to the square of the modulus of this complex number, the conjugate being generally denoted by the ". Sup.Right-Angle sign"), the new result will be represented by b _ij The new matrix of composition is recorded as matrix

It can be understood that g _t Can also be replaced by

In the statistical averaging, the statistical averaging may be performed for one or more factors of different time, different transmitting antennas, different frequencies, and the like. One transmitting antenna of the network equipment corresponds to one second channel estimation matrix, and one transmitting antenna corresponds to one second channel estimation matrix

Can be paired with

Statistically averaged over time with different transmit antennas of the network device, e.g. over a plurality of transmit antennas acquired over different time, different transmit antennas

Statistical averaging is performed.

In one example, the second channel estimation matrix is a column vector, e.g., g _t Is of dimension M _H M _V M _F M _T O _H O _V O _F O _T X1, or M _H M _V M _F X1, or M _H M _V O _H O _V X 1. Correspondingly, the second statistical mean energy is a column vector, and the dimension of the second statistical mean energy is, for example, M _H M _V M _F M _T O _H O _V O _F O _T X1, or M _H M _V M _F X1, or M _H M _V O _H O _V ×1。

Next for step 604: and determining a correlation process of a second power spectrum based on the second statistical average energy for introduction.

A mapping relationship exists between the second statistical average energy and the second power spectrum, and the mapping relationship satisfies the following formula:

Tω＝φ，

where ω is the second power spectrum, φ is the second statistical average energy, T is a mapping matrix, and T is associated with the third transformation matrix.

An alternative example, the second power spectrum is a column vector.

It will be appreciated that the second channel estimation matrix is based on F _H ，F _V ，F _F ，F _T And the mapping matrix T is also derived based on these matrices. In addition, the second power spectrum also represents a corresponding power spectrum, and the second power spectrum may be a combination of one or more of an angle power spectrum, a time delay power spectrum, and a doppler power spectrum. The angle power spectrum corresponds to a space domain, the delay power spectrum corresponds to a frequency domain, and the Doppler power spectrum corresponds to a time domain.

For example, when the third transformation matrix is based on a spatial domain matrix (e.g., F) _H ，F _V ) When obtained, the second power spectrum is an angular power spectrum.

For example, when the third transformation matrix is based on a frequency domain matrix (e.g., F) _F ) When obtained, the second power spectrum is a time delay power spectrum.

For example, when the third transformation matrix baseIn a time domain matrix (e.g. F) _T ) When obtained, the second power spectrum is a doppler power spectrum.

For example, when the third transformation matrix is based on a spatial domain matrix, a frequency domain matrix (e.g., F) _H ，F _V ，F _F ) When obtained, the second power spectrum is a combination of the angle power spectrum and the time delay power spectrum.

For example, when the third transformation matrix is based on a spatial domain matrix, a frequency domain matrix, a time domain matrix (e.g., F) _H ，F _V ，F _F ，F _T ) When obtained, the second power spectrum is a combination of an angle power spectrum, a delay power spectrum, and a doppler power spectrum.

And when the second power spectrum is a combined power spectrum of the angle power spectrum, the delay power spectrum and the Doppler power spectrum, the subsequently determined statistical covariance matrix of the uplink channel is a space, frequency and time combined statistical covariance matrix.

In an alternative example, each element in the second power spectrum is a non-negative real value. Therefore, the semi-positive determination of the determined statistical covariance matrix of the uplink channel can be ensured, and the accuracy of the determined statistical covariance matrix of the uplink channel can be improved.

The mapping matrix T being related to said third transformation matrix, e.g. mapping matrices T and F _H ，F _V ，F _F ，F _T Is correlated with one or more matrices in the array. For example, the mapping matrix T is based on F _H ，F _V ，F _F ，F _T And based on conjugation, conjugate transpose, hadamard product &, kronecker product

Is determined by one or more algorithms.

In an alternative example a of the method,

in an alternative example b of the method,

in an alternative example c of the use of the solution,

in an alternative example of this, the first,

in an alternative example of this, the first,

in an alternative example of this, the first,

in an alternative example of this, the first,

in the present application, a minimum L2 norm distance criterion, or a minimum KL divergence criterion, or a minimum L0 norm criterion may be employed to determine the second power spectrum based on the second statistical mean energy (e.g., estimate ω based on T and Φ). This is the same as the process of determining the first power spectrum based on the first statistical average energy by using the minimum L2-norm distance criterion, the minimum KL divergence criterion, or the minimum L0-norm criterion in the first embodiment, which can be referred to the description of the first embodiment and is not repeated.

Next for step 605: and determining a correlation process of a statistical covariance matrix of an uplink channel based on the second power spectrum and the fourth transformation matrix for introduction.

When determining the statistical covariance matrix of the uplink channel, one or more fourth transformation matrices may be used.

The type of the one or more fourth transform matrices may be Discrete Cosine Transform (DCT) matrices, or Hadamard transform matrices, or DFT matrices, or oversampled DFT matrices. It is noted that the type of the plurality of fourth transformation matrices is generally the same for these several types. The types of the third transformation matrix and the fourth transformation matrix may be the same or different.

When the type of the fourth transform matrix is a Discrete Cosine Transform (DCT) matrix, the fourth transform matrix is referred to as a fourth DCT matrix. When the type of the fourth transform matrix is a hadamard transform matrix, the fourth transform matrix is referred to as a fourth hadamard transform matrix. When the type of the fourth transform matrix is a discrete fourier transform DFT matrix, the fourth transform matrix is referred to as a fourth discrete fourier transform DFT matrix. When the type of the fourth transform matrix is the oversampled DFT matrix, the fourth transform matrix is referred to as a fourth oversampled DFT matrix.

In addition, a fourth transformation matrix may be obtained based on any one of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix. The at least one fourth transformation matrix may be obtained based on at least one type of matrix: a spatial domain transform matrix, a frequency domain transform matrix, a time domain transform matrix. A transform matrix used to determine the spatial domain type of the fourth transform matrix is referred to as a fourth spatial domain transform matrix, a transform matrix used to determine the frequency domain type of the fourth transform matrix is referred to as a fourth frequency domain transform matrix, and a transform matrix used to determine the time domain type of the fourth transform matrix is referred to as a fourth time domain transform matrix.

It is to be understood that, when the type of the at least one fourth transformation matrix is a Discrete Cosine Transformation (DCT) matrix, and the at least one fourth transformation matrix is obtained based on at least one type of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, the at least one fourth transformation matrix may be regarded as being obtained based on at least one type of a spatial domain DCT matrix, a frequency domain DCT matrix, and a time domain DCT matrix. When the type of the at least one fourth transformation matrix is an oversampled DFT matrix and the at least one fourth transformation matrix is obtained based on at least one type of a spatial domain transformation matrix, a frequency domain transformation matrix, and a time domain transformation matrix, the at least one fourth transformation matrix may be regarded as being obtained based on at least one type of a spatial domain oversampled DFT matrix, a frequency domain oversampled DFT matrix, and a time domain oversampled DFT matrix. The other types of matrices are similar and will not be repeated.

As for the types of the aforementioned matrices, the discrete cosine transform DCT matrix, or the hadamard transform matrix, or the DFT matrix, or the oversampled DFT matrix, the fourth transform matrix may be the same as or different from the third transform matrix, for example, the third transform matrix is a DCT matrix, and the fourth transform matrix is a hadamard transform matrix. The third transformation matrix and the fourth transformation matrix may be of the same type, and the third transformation matrix and the fourth transformation matrix may have the same or different specific contents. It should be noted that, the three types of spatial domain, frequency domain and time domain, the type of the third transformation matrix and the fourth transformation matrix are the same, for example, the third transformation matrix is obtained based on the spatial domain transformation matrix, and the fourth transformation matrix is also obtained based on the spatial domain transformation matrix, or the number of the third transformation matrix is 2, and the third transformation matrix is obtained based on the frequency domain transformation matrix and the time domain transformation matrix, respectively, and the number of the fourth transformation matrix is also 2, and the fourth transformation matrix is obtained based on the frequency domain transformation matrix and the time domain transformation matrix, respectively.

Recording a fourth transformation matrix obtained based on the spatial domain horizontal matrix as

Record the fourth transformation matrix obtained based on the spatial domain vertical matrix as

Dimension of M _V ×M _V O _V ，O _V Representing spatial domain vertical oversampling multiple, M _V Indicating the number of vertical antennas.

Record the fourth transformation matrix obtained based on the frequency domain matrix as

Record the fourth transformation matrix obtained based on the time domain matrix as

Optionally, the number and the dimension of the fourth transformation matrix are the same as those of the third transformation matrix.

A third transformation matrix (e.g., F) is introduced above _H ，F _V ，F _F ，F _T ) The third transformation matrix is used for transforming the downlink channel estimation matrix, and corresponds to the downlink. A fourth transformation matrix (e.g.,

) And corresponding to the uplink, the method is used for determining the statistical covariance matrix of the uplink channel.

Alternatively to this, the first and second parts may,

Exemplaryly,

the following formula is satisfied. Let F denote dimension MThe matrix of MO, its mth row nth column element is:

wherein M is an integer from 1 to M, and n is an integer from 1 to M O. Wherein M may correspond to M introduced above _H 、M _V 、M _F 、M _T (ii) a O may correspond to O introduced above _H 、O _V 、O _F 、O _T . For example, applying the formula to a generator matrix

Then M in the formula is M _H O in the formula is O _H 。

Similarly, they will not be described one by one.

When determining the statistical covariance matrix of the uplink channel based on the second power spectrum and the fourth transformation matrix:

e.g., based on one or more fourth transformation matrices, a second power spectrum, and a kronecker product

And performing one or more algorithms of transposition, conjugate transposition and diag to obtain a statistical covariance matrix of the uplink channel.

And multiplying by diag (omega) to obtain a statistical covariance matrix of the uplink channel.

Where ω is the second power spectrum, e.g., the second power spectrum is a column vector, and diag (ω) is used to indicate that the column vector is placed on a diagonal, e.g.

As another example, the kronecker product of a plurality of third transformation matrices

And obtaining a matrix, multiplying the matrix by diag (omega), and multiplying by the conjugate transpose of the matrix to obtain the statistical covariance of the uplink channel.

In an alternative example a, the statistical covariance matrix R of the uplink channel satisfies the following formula:

in an optional example b, the statistical covariance matrix of the uplink channel satisfies the following formula:

in an optional example c, the statistical covariance matrix of the uplink channel satisfies the following formula:

The method comprises the steps that a downlink channel estimation matrix is transformed through a third transformation matrix (such as a DFT matrix/an oversampling DFT matrix) of a space domain, a frequency domain and a time domain to obtain a second channel estimation matrix; obtaining a second statistical average energy based on the second channel estimation matrix; estimating to obtain an angle, a time delay and a Doppler power spectrum by utilizing the mapping relation between the second statistical average energy and the angle, the time delay and the Doppler power spectrum; and finally, reconstructing the spatial, frequency and time joint statistical covariance of the downlink channel based on a fourth transformation matrix (such as a DFT matrix/an oversampling DFT matrix) of a spatial domain, a frequency domain and a time domain corresponding to the angle, the time delay and the Doppler power spectrum and the downlink. Optionally, the angle, the delay, and the doppler power spectrum are estimated by using a mapping relationship between the second statistical average energy and the angle, the delay, and the doppler power spectrum, and combining a criterion under a non-negative constraint (for example, a minimum L2 norm distance criterion, a minimum KL divergence criterion, or a minimum L0 norm criterion).

The estimation method is simple, can be suitable for a scene of solving one or more statistical covariance in a space domain, a frequency domain and a time domain, and is easy to popularize.

The method of the embodiments of the present application is described above, and the apparatus of the embodiments of the present application is described below. The method and the device are based on the same technical conception, and because the principles of solving the problems of the method and the device are similar, the implementation of the device and the method can be mutually referred, and repeated parts are not repeated.

In the embodiment of the present application, according to the method example, the device may be divided into the functional modules, for example, the functional modules may be divided into the functional modules corresponding to the functions, or two or more functions may be integrated into one module. The modules can be realized in a form of hardware or a form of software functional modules. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and when the specific implementation is implemented, another division manner may be provided.

Based on the same technical concept as the method described above, referring to fig. 7, there is provided a schematic structural diagram of a communication apparatus 700, where the apparatus 700 may include: the processing module 710, optionally, further includes an interface module 720 and a storage module 730. The processing module 710 may be connected to the storage module 730 and the interface module 720 respectively, and the storage module 730 may also be connected to the interface module 720.

In one example, the interface module 720 can be defined as a receiving module and a sending module separately.

In an example, the apparatus 700 may be a network device, and may also be a chip or a functional unit applied in the network device. The apparatus 700 has any function of the network device in the method, for example, the apparatus 700 can execute each step executed by the network device in the method of fig. 4.

The interface module 720 may perform the receiving and sending actions performed by the network device in the above method embodiments.

The processing module 710 may perform other actions than the sending action and the receiving action among the actions performed by the network device in the above method embodiments.

In one example, the interface module 720 is configured to receive an uplink reference signal; the processing module 710 is configured to perform channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; transforming the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to an uplink channel; determining a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: performing statistical averaging on energy corresponding to part or all elements in the first channel estimation matrix respectively to obtain the energy; determining a first power spectrum based on the first statistical average energy, wherein a mapping relation exists between the first statistical average energy and the first power spectrum; determining a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to a downlink channel.

In one example, the interface module 720 is further configured to transmit data and/or reference signals based on the statistical covariance matrix of the downlink channel.

In one example, the storage module 730 may store computer executable instructions of a method performed by a network device to cause the processing module 710 and the interface module 720 to perform the method performed by the network device in the above example.

For example, a memory module may include one or more memories, which may be devices in one or more devices, circuits, or the like for storing programs or data. The storage module may be a register, a cache, or a RAM, etc., and the storage module may be integrated with the processing module. The memory module may be a ROM or other type of static storage device that may store static information and instructions, which may be separate from the processing module.

The transceiver module may be an input or output interface, a pin or a circuit, etc.

In an example, the apparatus 700 may be a terminal device, and may also be a chip or a functional unit applied in the terminal device. The apparatus 700 has any function of the terminal device in the method, for example, the apparatus 700 can execute each step executed by the terminal device in the method of fig. 6.

The interface module 720 may perform the receiving action and the sending action performed by the terminal device in the above method embodiments.

The processing module 710 may perform other actions than the sending action and the receiving action among the actions performed by the terminal device in the foregoing method embodiments.

In one example, the interface module 720 is configured to receive a downlink reference signal; the processing module 710 is configured to perform channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; transforming the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to a downlink channel; determining a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: performing statistical averaging on energies corresponding to part or all elements in the second channel estimation matrix to obtain the energy; determining a second power spectrum based on the second statistical average energy, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; determining a statistical covariance matrix of an uplink channel based on the second power spectrum and a fourth transformation matrix; the fourth transformation matrix is a matrix related to an uplink channel.

In one example, the interface module 720 is further configured to transmit data and/or reference signals based on the statistical covariance matrix of the uplink channel.

In one example, the storage module 730 may store computer-executable instructions of the method performed by the terminal device, so that the processing module 710 and the interface module 720 perform the method performed by the terminal device in the above example.

The apparatus applied to the network device and the apparatus applied to the terminal device according to the embodiments of the present application are introduced above, and possible product forms of the apparatus applied to the network device and the apparatus applied to the terminal device are introduced below. It should be understood that any product having any form of the features of the apparatus applied to the network device described above with reference to fig. 7 and any form of the features of the apparatus applied to the terminal device fall within the scope of the present application. It should be further understood that the following description is only exemplary, and should not limit the product form of the apparatus applied to the network device and the product form of the apparatus applied to the terminal device according to the embodiments of the present application.

As a possible product form, the device may be implemented by a generic bus architecture.

As shown in fig. 8, a schematic block diagram of a communication device 800 is provided.

The apparatus 800 may include: the processor 810, optionally, further includes a transceiver 820 and a memory 830. The transceiver 820 may be configured to receive a program or an instruction and transmit the program or the instruction to the processor 810, or the transceiver 820 may be configured to perform communication interaction between the apparatus 800 and other communication devices, such as interaction control signaling and/or service data. The transceiver 820 may be a code and/or data read-write transceiver, or the transceiver 820 may be a signal transmission transceiver between a processor and a transceiver. The processor 810 and the memory 830 are electrically coupled.

In an example, the apparatus 800 may be a network device, and may also be a chip applied to the network device. It should be understood that the apparatus has any function of the network device in the method, for example, the apparatus 800 can perform each step performed by the network device in the method of fig. 4. Illustratively, the memory 830 is used for storing computer programs; the processor 810 may be configured to invoke the computer programs or instructions stored in the memory 830 to perform the methods performed by the network devices in the above examples, or perform the methods performed by the network devices in the above examples through the transceiver 820.

In one example, the apparatus 800 may be a terminal device, and may also be a chip applied in the terminal device. It should be understood that the apparatus has any function of the terminal device in the method described above, for example, the apparatus 800 can perform each step performed by the terminal device in the method of fig. 6 described above. Illustratively, the memory 830 is used to store computer programs; the processor 810 may be configured to call the computer program or the instructions stored in the memory 830 to perform the method performed by the terminal device in the above example, or perform the method performed by the terminal device in the above example through the transceiver 820.

The processing module 710 of fig. 7 may be implemented by the processor 810.

The interface module 720 of fig. 7 may be implemented by the transceiver 820. Alternatively, the transceiver 820 is divided into a receiver that performs a receiving function of the interface module and a transmitter that performs a transmitting function of the interface module.

The storage module 730 of fig. 7 may be implemented by the memory 830.

As one possible product form, an apparatus may be implemented by a general purpose processor (which may also be referred to as a chip or a system of chips).

In one possible implementation, a general-purpose processor implementing an apparatus for a network device or an apparatus for a terminal device includes: processing circuitry (which may also be referred to as a processor); optionally, the method further includes: an input/output interface in internal communication with the processing circuit, and a storage medium (the storage medium may also be referred to as a memory) for storing instructions executed by the processing circuit to perform the method executed by the network device or the terminal device in the above example.

The processing module 710 of fig. 7 may be implemented by a processing circuit.

The interface module 720 in fig. 7 may be implemented by an input-output interface. Or the input and output interface is divided into an input interface and an output interface, the input interface executes the receiving function of the interface module, and the output interface executes the sending function of the interface module.

The storage module 730 of fig. 7 may be implemented by a storage medium.

As a possible product form, the apparatus according to the embodiment of the present application may be implemented using: one or more FPGAs (field programmable gate arrays), PLDs (programmable logic devices), controllers, state machines, gate logic, discrete hardware components, any other suitable circuitry, or any combination of circuitry capable of performing the various functions described throughout this application.

Embodiments of the present application also provide a computer-readable storage medium storing a computer program, which when executed by a computer, can cause the computer to perform the above method for determining statistical covariance of a channel. Or the following steps: the computer program includes instructions for implementing the above-described method of determining statistical covariance of a channel.

An embodiment of the present application further provides a computer program product, including: computer program code which, when run on a computer, causes the computer to perform the method of determining statistical covariance of a channel as provided above.

An embodiment of the present application further provides a communication system, where the communication system includes: terminal equipment and network equipment for executing the method for determining the statistical covariance of the channel.

In addition, the processor mentioned in the embodiment of the present application may be a Central Processing Unit (CPU), a baseband processor and a CPU may be integrated together or separated, and may also be a Network Processor (NP) or a combination of the CPU and the NP. The processor may further include a hardware chip or other general purpose processor. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The aforementioned PLDs may be Complex Programmable Logic Devices (CPLDs), field-programmable gate arrays (FPGAs), general Array Logic (GAL) and other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., or any combination thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory referred to in the embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double Data Rate Synchronous Dynamic random access memory (DDR SDRAM), enhanced Synchronous SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

The transceiver mentioned in the embodiments of the present application may include a separate transmitter and/or a separate receiver, or may be an integrated transmitter and receiver. The transceivers may operate under the direction of a corresponding processor. Alternatively, the sender may correspond to a transmitter in the physical device, and the receiver may correspond to a receiver in the physical device.

Those of ordinary skill in the art will appreciate that the various method steps and elements described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both, and that the steps and elements of the embodiments are generally described in the foregoing description as functional or software interchange, for the purpose of clearly illustrating the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application may substantially or partially contribute to the prior art, or all or part of the technical solutions may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various changes and modifications may be made in the embodiments of the present application without departing from the scope of the embodiments of the present application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include such modifications and variations.

Claims

1. A method for determining channel statistics covariance, applied to a network device, comprising:

performing channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix;

transforming the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to an uplink channel;

determining first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: performing statistical averaging on energy corresponding to part or all elements in the first channel estimation matrix respectively to obtain the energy;

determining a first power spectrum based on the first statistical average energy, wherein a mapping relation exists between the first statistical average energy and the first power spectrum;

determining a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to a downlink channel.

2. The method of claim 1, further comprising:

and sending data and/or reference signals based on the statistical covariance matrix of the downlink channel.

3. The method of claim 1 or 2, wherein each element in the first power spectrum is a non-negative real number value.

4. The method of any of claims 1-3, wherein the first transformation matrix is any of: the system comprises a first Discrete Cosine Transform (DCT) matrix, a first Hadamard transform (Hadamard) matrix, a first Discrete Fourier Transform (DFT) matrix and a first oversampling DFT matrix;

the second transformation matrix is any one of: a second Discrete Cosine Transform (DCT) matrix, a second Hadamard transform matrix, a second Discrete Fourier Transform (DFT) matrix, and a second oversampled DFT matrix.

5. The method of any of claims 1-4, wherein the first transformation matrix is obtained based on at least one of:

a first spatial domain transformation matrix, a first frequency domain transformation matrix, a first time domain transformation matrix;

the second transformation matrix is obtained based on at least one of:

a second spatial domain transform matrix, a second frequency domain transform matrix, and a second time domain transform matrix.

6. The method of any of claims 1-5, wherein a mapping between the first statistical average energy and the first power spectrum satisfies the following equation:

t ω = Φ, where ω is the first power spectrum, Φ is the first statistical average energy, T is a mapping matrix, and T is associated with the first transformation matrix.

7. A method for determining channel statistic covariance, which is applied to a terminal device, includes:

performing channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix;

transforming the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to a downlink channel;

determining a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: performing statistical averaging on energies corresponding to part or all elements in the second channel estimation matrix to obtain the energy;

determining a second power spectrum based on the second statistical average energy, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum;

determining a statistical covariance matrix of an uplink channel based on the second power spectrum and a fourth transformation matrix; the fourth transformation matrix is a matrix related to an uplink channel.

8. The method of claim 7, further comprising:

and sending data and/or reference signals based on the statistical covariance matrix of the uplink channel.

9. The method of claim 7 or 8, wherein each element in the second power spectrum is a non-negative real number value.

10. The method of any of claims 7-9, wherein the third transformation matrix is any of: a third Discrete Cosine Transform (DCT) matrix, a third Hadamard transform matrix, a third Discrete Fourier Transform (DFT) matrix and a third oversampled DFT matrix;

the fourth transformation matrix is any one of: a fourth Discrete Cosine Transform (DCT) matrix, a fourth Hadamard transform (Hadamard transform) matrix, a fourth Discrete Fourier Transform (DFT) matrix and a fourth oversampled Discrete Fourier Transform (DFT) matrix.

11. The method according to any of claims 7-10, wherein the third transformation matrix is obtained based on at least one of:

a third spatial domain transformation matrix, a third frequency domain transformation matrix and a third time domain transformation matrix;

the fourth transformation matrix is obtained based on at least one of:

a fourth spatial domain transform matrix, a fourth frequency domain transform matrix, and a fourth time domain transform matrix.

12. The method of any of claims 7-11, wherein a mapping between the second statistical mean energy and the second power spectrum satisfies the following equation:

t ω = Φ, where ω is the second power spectrum, Φ is the second statistical mean energy, T is a mapping matrix, and T is associated with the third transformation matrix.

13. A communications apparatus, comprising:

the interface module is used for receiving an uplink reference signal;

the processing module is used for carrying out channel estimation based on the received uplink reference signal to obtain an uplink channel estimation matrix; transforming the uplink channel estimation matrix based on a first transformation matrix to obtain a first channel estimation matrix; the first transformation matrix is a matrix related to an uplink channel; determining a first statistical average energy corresponding to the first channel estimation matrix; the first statistical average energy is: respectively carrying out statistical averaging on energy corresponding to part or all elements in the first channel estimation matrix to obtain the energy; determining a first power spectrum based on the first statistical average energy, wherein a mapping relation exists between the first statistical average energy and the first power spectrum; determining a statistical covariance matrix of a downlink channel based on the first power spectrum and the second transformation matrix; the second transformation matrix is a matrix related to a downlink channel.

14. The apparatus of claim 13, wherein the interface module is further configured to transmit data and/or reference signals based on a statistical covariance matrix of the downlink channel.

15. The apparatus according to claim 13 or 14, wherein each element in the first power spectrum is a non-negative real value.

16. The apparatus of any one of claims 13-15, wherein the first transformation matrix is any one of: the system comprises a first Discrete Cosine Transform (DCT) matrix, a first Hadamard transform (Hadamard) matrix, a first Discrete Fourier Transform (DFT) matrix and a first oversampling DFT matrix;

17. The apparatus of any one of claims 13-16, wherein the first transformation matrix is obtained based on at least one of:

a first spatial domain transformation matrix, a first frequency domain transformation matrix and a first time domain transformation matrix;

the second transformation matrix is obtained based on at least one of:

a second spatial domain transform matrix, a second frequency domain transform matrix, a second time domain transform matrix.

18. The apparatus of any of claims 13-17, wherein a mapping between the first statistical mean energy and the first power spectrum satisfies the following equation:

tmo = phi, where omega is the first power spectrum, phi is the first statistical mean energy, T is a mapping matrix, and T is associated with the first transformation matrix.

19. A communications apparatus, comprising:

the interface module is used for receiving a downlink reference signal;

the processing module is used for carrying out channel estimation based on the received downlink reference signal to obtain a downlink channel estimation matrix; transforming the downlink channel estimation matrix based on a third transformation matrix to obtain a second channel estimation matrix; the third transformation matrix is a matrix related to a downlink channel; determining a second statistical average energy corresponding to the second channel estimation matrix; the second statistical average energy is: performing statistical averaging on energy corresponding to part or all elements in the second channel estimation matrix respectively to obtain the energy; determining a second power spectrum based on the second statistical average energy, wherein a mapping relationship exists between the second statistical average energy and the second power spectrum; determining a statistical covariance matrix of an uplink channel based on the second power spectrum and a fourth transformation matrix; the fourth transformation matrix is a matrix related to an uplink channel.

20. The apparatus of claim 19, wherein the interface module is further configured to transmit data and/or reference signals based on a statistical covariance matrix of the uplink channel.

21. The apparatus according to claim 19 or 20, wherein each element in the second power spectrum is a non-negative real number value.

22. The apparatus of any one of claims 19-21, wherein the third transformation matrix is any one of: a third Discrete Cosine Transform (DCT) matrix, a third Hadamard transform matrix, a third Discrete Fourier Transform (DFT) matrix and a third oversampled DFT matrix;

23. The apparatus of any one of claims 19-22, wherein the third transformation matrix is obtained based on at least one of:

the fourth transformation matrix is obtained based on at least one of:

24. The apparatus of any of claims 19-23, wherein a mapping between the second statistical mean energy and the second power spectrum satisfies the following equation:

25. A communications apparatus comprising a processor coupled with a memory;

the memory for storing computer programs or instructions;

the processor for executing part or all of the computer program or instructions in the memory, for implementing the method of any one of claims 1-6, or for implementing the method of any one of claims 7-12, when the part or all of the computer program or instructions are executed.

26. A communication device comprising a processor and a memory;

the memory for storing computer programs or instructions;

27. A system-on-chip, the system-on-chip comprising: a processing circuit; the processing circuit is coupled with a storage medium;

processing circuitry for executing part or all of the computer program or instructions in the storage medium, for implementing the method of any of claims 1-6, or for implementing the method of any of claims 7-12, when the part or all of the computer program or instructions is executed.

28. A computer-readable storage medium for storing a computer program comprising instructions for implementing the method of any one of claims 1-6 or instructions for implementing the method of any one of claims 7-12.

29. A computer program product, the computer program product comprising: computer program code for causing a computer to perform the method of any of claims 1-6 or the method of any of claims 7-12 when said computer program code is run on a computer.